Quantitative evaluation of carbon emissions with mining technology development: a case study of an iron mine in China.

To quantitatively evaluate the carbon emission effects of various underground mining schemes in metal mines, a carbon emission calculation model specifically for underground metal mines was established. The carbon emissions stemming from the mine's production process were categorized into three components: carbon emissions from the production of consumed materials, fuel, and electricity; carbon emissions resulting from fuel combustion and explosive explosions, and the reduction of CO2 absorption due to the occupation of the surface industrial site. Subsequently, the carbon emission impact of underground metal mines was assessed using an example from an iron mine in Anhui Province, China. The results showed: (1) Among the underground mining processes, electricity consumption emerged as the primary source of carbon emissions. This underscores the potential for significant carbon emission reduction through the implementation of innovative electric power technologies in underground metal mines. (2) Mining methods with higher productivity showed clear advantages. They not only contribute to the reduction of carbon emissions per kiloton of ore from multiple perspectives but also led to a shorter mine lifespan and decreased CO2 absorption by woodlands occupied by the surface industrial site. Furthermore, these methods resulted in lower carbon emissions throughout the mine's lifespan. (3) Backfill mining proved to be effective in curbing tailings emissions and reducing the required area for a tailings pond. Consequently, this approach minimizes the CO2 absorption by woodlands occupied by the tailings pond.

Mechanical properties and damage constitutive model of phosphogypsum-based cemented backfill under hydrochemical action.

In order to analyze the early mechanical properties and damage characteristics of phosphogypsum-based cemented backfill (PCB) under hydrochemical action, hydrochemical erosion and uniaxial compression strength (UCS) tests were carried out with HCl solution, NaOH solution, and water respectively. The damage degree is defined by taking the effective bearing area of the soluble cements of PCB under hydrochemistry action as the chemical damage variable, and the modified damage parameter α, which reflects the damage development characteristics, is introduced to construct the damage constitutive model of PCB considering chemical damage and load damage, and the theoretical model is verified with the experimental results. The results show that the damage constitutive model curves of PCB under different hydrochemical action are in good agreement with the experimental results, which verifies the correctness of the theoretical model. When the modified damage parameter α decreases from 1.0 to 0.8, the residual load-bearing capacity of PCB gradually increases, with the damage values of PCB samples in HCl solution and water gradually increasing before the peak and decreasing after the peak, while the damage values of PCB samples in NaOH solution show an overall increasing trend before and after the peak. The slope of the post peak curve of PCB decreases with increasing model parameter n. The results of the study can provide theoretical support and practical guidance for the strength design, long-term erosion deformation, and prediction of PCB in hydrochemical environment.

Design and Analysis of a Novel Piezoceramic Stack-based Smart Aggregate.

A novel piezoceramic stack-based smart aggregate (PiSSA) with piezoceramic wafers in series or parallel connection is developed to increase the efficiency and output performance over the conventional smart aggregate with only one piezoelectric patch. Due to the improvement, PiSSA is suitable for situations where the stress waves easily attenuate. In PiSSA, the piezoelectric wafers are electrically connected in series or parallel, and three types of piezoelectric wafers with different electrode patterns are designed for easy connection. Based on the theory of piezo-elasticity, a simplified one-dimensional model is derived to study the electromechanical, transmitting and sensing performance of PiSSAs with the wafers in series and parallel connection, and the model was verified by experiments. The theoretical results reveal that the first resonance frequency of PiSSAs in series and parallel decreases as the number or thickness of the PZT wafers increases, and the first electromechanical coupling factor increases firstly and then decrease gradually as the number or thickness increases. The results also show that both the first resonance frequency and the first electromechanical coupling factor of PiSSA in series and parallel change no more than 0.87% as the Young's modulus of the epoxy increases from 0.5 to 1.5 times 3.2 GPa, which is helpful for the fabrication of PiSSAs. In addition, the displacement output of PiSSAs in parallel is about 2.18-22.49 times that in series at 1-50 kHz, while the voltage output of PiSSAs in parallel is much less than that in parallel, which indicates that PiSSA in parallel is much more suitable for working as an actuator to excite stress waves and PiSSA in series is suitable for working as a sensor to detect the waves. All the results demonstrate that the connecting type, number and thickness of the PZT wafers should be carefully selected to increase the efficiency and output of PiSSA actuators and sensors. This study contributes to providing a method to investigate the characteristics and optimize the structural parameters of the proposed PiSSAs.

Multilayer Hybrid Deep-Learning Method for Waste Classification and Recycling.

This study proposes a multilayer hybrid deep-learning system (MHS) to automatically sort waste disposed of by individuals in the urban public area. This system deploys a high-resolution camera to capture waste image and sensors to detect other useful feature information. The MHS uses a CNN-based algorithm to extract image features and a multilayer perceptrons (MLP) method to consolidate image features and other feature information to classify wastes as recyclable or the others. The MHS is trained and validated against the manually labelled items, achieving overall classification accuracy higher than 90% under two different testing scenarios, which significantly outperforms a reference CNN-based method relying on image-only inputs.